Overview

Dataset statistics

Number of variables34
Number of observations1000
Missing cells370
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory265.8 KiB
Average record size in memory272.1 B

Variable types

Numeric15
Categorical19

Alerts

Unique Identifier is highly correlated with ProductHigh correlation
How much do you like or dislike the PACKAGING for this product? - value is highly correlated with How much do you like or dislike the PACKAGING for this product? - label and 2 other fieldsHigh correlation
Open the packaging. How much do you like or dislike the APPEARANCE of the products? - value is highly correlated with How much do you like or dislike the PACKAGING for this product? - label and 10 other fieldsHigh correlation
Taste the products. How do you like the products OVERALL? - value is highly correlated with Open the packaging. How much do you like or dislike the APPEARANCE of the products? - label and 12 other fieldsHigh correlation
How much do you like or dislike the FLAVOR of the products? - value is highly correlated with Open the packaging. How much do you like or dislike the APPEARANCE of the products? - label and 12 other fieldsHigh correlation
How much do you like or dislike the TEXTURE of the products? - value is highly correlated with Open the packaging. How much do you like or dislike the APPEARANCE of the products? - label and 10 other fieldsHigh correlation
How much do you like or dislike the AFTERTASTE of the products? - value is highly correlated with Open the packaging. How much do you like or dislike the APPEARANCE of the products? - label and 10 other fieldsHigh correlation
Product is highly correlated with Unique IdentifierHigh correlation
What is your gender? - label is highly correlated with What is your gender? - valueHigh correlation
What is your gender? - value is highly correlated with What is your gender? - labelHigh correlation
What is your age group? - label is highly correlated with What is your age group? - valueHigh correlation
What is your age group? - value is highly correlated with What is your age group? - labelHigh correlation
How much do you like or dislike the PACKAGING for this product? - label is highly correlated with How much do you like or dislike the PACKAGING for this product? - value and 2 other fieldsHigh correlation
Before tasting the product, how likely would you be to purchase this product, if you didn't have to buy it for this study? - label is highly correlated with Before tasting the product, how likely would you be to purchase this product, if you didn't have to buy it for this study? - value and 2 other fieldsHigh correlation
Before tasting the product, how likely would you be to purchase this product, if you didn't have to buy it for this study? - value is highly correlated with Before tasting the product, how likely would you be to purchase this product, if you didn't have to buy it for this study? - label and 2 other fieldsHigh correlation
Open the packaging. How much do you like or dislike the APPEARANCE of the products? - label is highly correlated with How much do you like or dislike the PACKAGING for this product? - label and 10 other fieldsHigh correlation
How would you rate the AMOUNT OF SEASONING of the products? - label is highly correlated with How would you rate the AMOUNT OF SEASONING of the products? - value and 4 other fieldsHigh correlation
How would you rate the AMOUNT OF SEASONING of the products? - value is highly correlated with How would you rate the AMOUNT OF SEASONING of the products? - label and 4 other fieldsHigh correlation
Taste the products. How do you like the products OVERALL? - label is highly correlated with Open the packaging. How much do you like or dislike the APPEARANCE of the products? - label and 12 other fieldsHigh correlation
How much do you like or dislike the FLAVOR of the products? - label is highly correlated with Open the packaging. How much do you like or dislike the APPEARANCE of the products? - label and 12 other fieldsHigh correlation
How would you rate the FLAVOR of the products? - label is highly correlated with How would you rate the AMOUNT OF SEASONING of the products? - label and 8 other fieldsHigh correlation
How would you rate the FLAVOR of the products? - value is highly correlated with How would you rate the AMOUNT OF SEASONING of the products? - label and 8 other fieldsHigh correlation
How much do you like or dislike the TEXTURE of the products? - label is highly correlated with Open the packaging. How much do you like or dislike the APPEARANCE of the products? - label and 10 other fieldsHigh correlation
How much do you like or dislike the AFTERTASTE of the products? - label is highly correlated with Open the packaging. How much do you like or dislike the APPEARANCE of the products? - label and 10 other fieldsHigh correlation
Now that you have tasted the products, how likely would you be to re-purchase the products? - label is highly correlated with Before tasting the product, how likely would you be to purchase this product, if you didn't have to buy it for this study? - label and 14 other fieldsHigh correlation
Now that you have tasted the products, how likely would you be to re-purchase the products? - value is highly correlated with Before tasting the product, how likely would you be to purchase this product, if you didn't have to buy it for this study? - label and 14 other fieldsHigh correlation
Taste the products. How do you like the products OVERALL? - label has 98 (9.8%) missing values Missing
Taste the products. How do you like the products OVERALL? - value has 98 (9.8%) missing values Missing
Now that you have tasted the products, how likely would you be to re-purchase the products? - label has 87 (8.7%) missing values Missing
Now that you have tasted the products, how likely would you be to re-purchase the products? - value has 87 (8.7%) missing values Missing
Unique Identifier is uniformly distributed Uniform
Product is uniformly distributed Uniform
Unique Identifier has unique values Unique

Reproduction

Analysis started2022-12-12 18:42:02.503369
Analysis finished2022-12-12 18:44:40.703126
Duration2 minutes and 38.2 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Unique Identifier
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100500.5
Minimum100001
Maximum101000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-12-12T20:44:40.990973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum100001
5-th percentile100050.95
Q1100250.75
median100500.5
Q3100750.25
95-th percentile100950.05
Maximum101000
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.8194361
Coefficient of variation (CV)0.002873810937
Kurtosis-1.2
Mean100500.5
Median Absolute Deviation (MAD)250
Skewness0
Sum100500500
Variance83416.66667
MonotonicityStrictly increasing
2022-12-12T20:44:41.502290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000011
 
0.1%
1006721
 
0.1%
1006591
 
0.1%
1006601
 
0.1%
1006611
 
0.1%
1006621
 
0.1%
1006631
 
0.1%
1006641
 
0.1%
1006651
 
0.1%
1006661
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
1000011
0.1%
1000021
0.1%
1000031
0.1%
1000041
0.1%
1000051
0.1%
1000061
0.1%
1000071
0.1%
1000081
0.1%
1000091
0.1%
1000101
0.1%
ValueCountFrequency (%)
1010001
0.1%
1009991
0.1%
1009981
0.1%
1009971
0.1%
1009961
0.1%
1009951
0.1%
1009941
0.1%
1009931
0.1%
1009921
0.1%
1009911
0.1%

Product
Categorical

HIGH CORRELATION
UNIFORM

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
A
200 
B
200 
C
200 
D
200 
E
200 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
A200
20.0%
B200
20.0%
C200
20.0%
D200
20.0%
E200
20.0%

Length

2022-12-12T20:44:41.933478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:44:42.289492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
a200
20.0%
b200
20.0%
c200
20.0%
d200
20.0%
e200
20.0%

Most occurring characters

ValueCountFrequency (%)
A200
20.0%
B200
20.0%
C200
20.0%
D200
20.0%
E200
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A200
20.0%
B200
20.0%
C200
20.0%
D200
20.0%
E200
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A200
20.0%
B200
20.0%
C200
20.0%
D200
20.0%
E200
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A200
20.0%
B200
20.0%
C200
20.0%
D200
20.0%
E200
20.0%

What is your gender? - label
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Female
865 
Male
135 

Length

Max length6
Median length6
Mean length5.73
Min length4

Characters and Unicode

Total characters5730
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female865
86.5%
Male135
 
13.5%

Length

2022-12-12T20:44:42.696070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:44:43.076038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
female865
86.5%
male135
 
13.5%

Most occurring characters

ValueCountFrequency (%)
e1865
32.5%
a1000
17.5%
l1000
17.5%
F865
15.1%
m865
15.1%
M135
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4730
82.5%
Uppercase Letter1000
 
17.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1865
39.4%
a1000
21.1%
l1000
21.1%
m865
18.3%
Uppercase Letter
ValueCountFrequency (%)
F865
86.5%
M135
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Latin5730
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1865
32.5%
a1000
17.5%
l1000
17.5%
F865
15.1%
m865
15.1%
M135
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1865
32.5%
a1000
17.5%
l1000
17.5%
F865
15.1%
m865
15.1%
M135
 
2.4%

What is your gender? - value
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2
865 
1
135 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2865
86.5%
1135
 
13.5%

Length

2022-12-12T20:44:43.398480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:44:43.739624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2865
86.5%
1135
 
13.5%

Most occurring characters

ValueCountFrequency (%)
2865
86.5%
1135
 
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2865
86.5%
1135
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2865
86.5%
1135
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2865
86.5%
1135
 
13.5%

What is your age group? - label
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
35-44
358 
18-34
337 
45-55
237 
56-64
68 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5000
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row35-44
2nd row45-55
3rd row18-34
4th row45-55
5th row18-34

Common Values

ValueCountFrequency (%)
35-44358
35.8%
18-34337
33.7%
45-55237
23.7%
56-6468
 
6.8%

Length

2022-12-12T20:44:44.058679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:44:44.350368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
35-44358
35.8%
18-34337
33.7%
45-55237
23.7%
56-6468
 
6.8%

Most occurring characters

ValueCountFrequency (%)
41358
27.2%
51137
22.7%
-1000
20.0%
3695
13.9%
1337
 
6.7%
8337
 
6.7%
6136
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4000
80.0%
Dash Punctuation1000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
41358
34.0%
51137
28.4%
3695
17.4%
1337
 
8.4%
8337
 
8.4%
6136
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
-1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
41358
27.2%
51137
22.7%
-1000
20.0%
3695
13.9%
1337
 
6.7%
8337
 
6.7%
6136
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41358
27.2%
51137
22.7%
-1000
20.0%
3695
13.9%
1337
 
6.7%
8337
 
6.7%
6136
 
2.7%

What is your age group? - value
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
3
358 
2
337 
4
237 
5
68 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row2
4th row4
5th row2

Common Values

ValueCountFrequency (%)
3358
35.8%
2337
33.7%
4237
23.7%
568
 
6.8%

Length

2022-12-12T20:44:44.695901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:44:45.081388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3358
35.8%
2337
33.7%
4237
23.7%
568
 
6.8%

Most occurring characters

ValueCountFrequency (%)
3358
35.8%
2337
33.7%
4237
23.7%
568
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3358
35.8%
2337
33.7%
4237
23.7%
568
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3358
35.8%
2337
33.7%
4237
23.7%
568
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3358
35.8%
2337
33.7%
4237
23.7%
568
 
6.8%
Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Like Very Much (8)
360 
Like Moderately (7)
239 
Like Extremely (9)
223 
Like Slightly (6)
100 
Neither Like nor Dislike (5)
49 
Other values (3)
 
29

Length

Max length28
Median length18
Mean length18.694
Min length17

Characters and Unicode

Total characters18694
Distinct characters36
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowNeither Like nor Dislike (5)
2nd rowLike Very Much (8)
3rd rowLike Very Much (8)
4th rowLike Extremely (9)
5th rowLike Very Much (8)

Common Values

ValueCountFrequency (%)
Like Very Much (8)360
36.0%
Like Moderately (7)239
23.9%
Like Extremely (9)223
22.3%
Like Slightly (6)100
 
10.0%
Neither Like nor Dislike (5)49
 
4.9%
Dislike Slightly (4)25
 
2.5%
Dislike Moderately (3)3
 
0.3%
Dislike Extremely (1)1
 
0.1%

Length

2022-12-12T20:44:46.037952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:44:46.474030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
like971
28.1%
very360
 
10.4%
much360
 
10.4%
8360
 
10.4%
moderately242
 
7.0%
7239
 
6.9%
extremely224
 
6.5%
9223
 
6.4%
slightly125
 
3.6%
6100
 
2.9%
Other values (7)254
 
7.3%

Most occurring characters

ValueCountFrequency (%)
2458
13.1%
e2439
13.0%
i1301
 
7.0%
k1049
 
5.6%
(1000
 
5.3%
)1000
 
5.3%
L971
 
5.2%
y951
 
5.1%
r924
 
4.9%
l794
 
4.2%
Other values (26)5807
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10827
57.9%
Space Separator2458
 
13.1%
Uppercase Letter2409
 
12.9%
Open Punctuation1000
 
5.3%
Close Punctuation1000
 
5.3%
Decimal Number1000
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2439
22.5%
i1301
12.0%
k1049
9.7%
y951
 
8.8%
r924
 
8.5%
l794
 
7.3%
t640
 
5.9%
h534
 
4.9%
u360
 
3.3%
c360
 
3.3%
Other values (8)1475
13.6%
Decimal Number
ValueCountFrequency (%)
8360
36.0%
7239
23.9%
9223
22.3%
6100
 
10.0%
549
 
4.9%
425
 
2.5%
33
 
0.3%
11
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
L971
40.3%
M602
25.0%
V360
 
14.9%
E224
 
9.3%
S125
 
5.2%
D78
 
3.2%
N49
 
2.0%
Space Separator
ValueCountFrequency (%)
2458
100.0%
Open Punctuation
ValueCountFrequency (%)
(1000
100.0%
Close Punctuation
ValueCountFrequency (%)
)1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13236
70.8%
Common5458
29.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2439
18.4%
i1301
9.8%
k1049
 
7.9%
L971
 
7.3%
y951
 
7.2%
r924
 
7.0%
l794
 
6.0%
t640
 
4.8%
M602
 
4.5%
h534
 
4.0%
Other values (15)3031
22.9%
Common
ValueCountFrequency (%)
2458
45.0%
(1000
18.3%
)1000
18.3%
8360
 
6.6%
7239
 
4.4%
9223
 
4.1%
6100
 
1.8%
549
 
0.9%
425
 
0.5%
33
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII18694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2458
13.1%
e2439
13.0%
i1301
 
7.0%
k1049
 
5.6%
(1000
 
5.3%
)1000
 
5.3%
L971
 
5.2%
y951
 
5.1%
r924
 
4.9%
l794
 
4.2%
Other values (26)5807
31.1%
Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.515
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-12-12T20:44:46.824801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median8
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.26228696
Coefficient of variation (CV)0.16796899
Kurtosis1.145100253
Mean7.515
Median Absolute Deviation (MAD)1
Skewness-1.010784136
Sum7515
Variance1.593368368
MonotonicityNot monotonic
2022-12-12T20:44:47.127813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
8360
36.0%
7239
23.9%
9223
22.3%
6100
 
10.0%
549
 
4.9%
425
 
2.5%
33
 
0.3%
11
 
0.1%
ValueCountFrequency (%)
11
 
0.1%
33
 
0.3%
425
 
2.5%
549
 
4.9%
6100
 
10.0%
7239
23.9%
8360
36.0%
9223
22.3%
ValueCountFrequency (%)
9223
22.3%
8360
36.0%
7239
23.9%
6100
 
10.0%
549
 
4.9%
425
 
2.5%
33
 
0.3%
11
 
0.1%
Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.268
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-12-12T20:44:47.485930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median7
Q38
95-th percentile8
Maximum9
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.779703162
Coefficient of variation (CV)0.2839347737
Kurtosis-0.8025680246
Mean6.268
Median Absolute Deviation (MAD)1
Skewness-0.6037771088
Sum6268
Variance3.167343343
MonotonicityNot monotonic
2022-12-12T20:44:47.776104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
8346
34.6%
5195
19.5%
7181
18.1%
3104
 
10.4%
698
 
9.8%
447
 
4.7%
916
 
1.6%
213
 
1.3%
ValueCountFrequency (%)
213
 
1.3%
3104
 
10.4%
447
 
4.7%
5195
19.5%
698
 
9.8%
7181
18.1%
8346
34.6%
916
 
1.6%
ValueCountFrequency (%)
916
 
1.6%
8346
34.6%
7181
18.1%
698
 
9.8%
5195
19.5%
447
 
4.7%
3104
 
10.4%
213
 
1.3%
Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.175
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-12-12T20:44:48.035042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median6
Q38
95-th percentile8
Maximum9
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.696836543
Coefficient of variation (CV)0.274791343
Kurtosis-0.7449215087
Mean6.175
Median Absolute Deviation (MAD)1
Skewness-0.489523227
Sum6175
Variance2.879254254
MonotonicityNot monotonic
2022-12-12T20:44:48.430677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
8289
28.9%
5230
23.0%
7190
19.0%
6126
12.6%
387
 
8.7%
449
 
4.9%
916
 
1.6%
213
 
1.3%
ValueCountFrequency (%)
213
 
1.3%
387
 
8.7%
449
 
4.9%
5230
23.0%
6126
12.6%
7190
19.0%
8289
28.9%
916
 
1.6%
ValueCountFrequency (%)
916
 
1.6%
8289
28.9%
7190
19.0%
6126
12.6%
5230
23.0%
449
 
4.9%
387
 
8.7%
213
 
1.3%
Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.295
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-12-12T20:44:48.754679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median7
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.715493491
Coefficient of variation (CV)0.2725168373
Kurtosis-0.5796709765
Mean6.295
Median Absolute Deviation (MAD)1
Skewness-0.6493695393
Sum6295
Variance2.942917918
MonotonicityNot monotonic
2022-12-12T20:44:49.134519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8318
31.8%
7210
21.0%
5183
18.3%
6119
 
11.9%
387
 
8.7%
454
 
5.4%
917
 
1.7%
210
 
1.0%
12
 
0.2%
ValueCountFrequency (%)
12
 
0.2%
210
 
1.0%
387
 
8.7%
454
 
5.4%
5183
18.3%
6119
 
11.9%
7210
21.0%
8318
31.8%
917
 
1.7%
ValueCountFrequency (%)
917
 
1.7%
8318
31.8%
7210
21.0%
6119
 
11.9%
5183
18.3%
454
 
5.4%
387
 
8.7%
210
 
1.0%
12
 
0.2%
Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.923
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-12-12T20:44:49.519891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q35
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.735535388
Coefficient of variation (CV)0.4424000479
Kurtosis-0.2080430348
Mean3.923
Median Absolute Deviation (MAD)1
Skewness0.8117094103
Sum3923
Variance3.012083083
MonotonicityNot monotonic
2022-12-12T20:44:49.870950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3335
33.5%
2199
19.9%
5178
17.8%
4111
 
11.1%
761
 
6.1%
854
 
5.4%
653
 
5.3%
19
 
0.9%
ValueCountFrequency (%)
19
 
0.9%
2199
19.9%
3335
33.5%
4111
 
11.1%
5178
17.8%
653
 
5.3%
761
 
6.1%
854
 
5.4%
ValueCountFrequency (%)
854
 
5.4%
761
 
6.1%
653
 
5.3%
5178
17.8%
4111
 
11.1%
3335
33.5%
2199
19.9%
19
 
0.9%
Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.236
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-12-12T20:44:50.245718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q33
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.531224559
Coefficient of variation (CV)0.4731843506
Kurtosis1.697225621
Mean3.236
Median Absolute Deviation (MAD)1
Skewness1.375782727
Sum3236
Variance2.344648649
MonotonicityNot monotonic
2022-12-12T20:44:50.613372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3424
42.4%
2285
28.5%
597
 
9.7%
459
 
5.9%
145
 
4.5%
632
 
3.2%
830
 
3.0%
728
 
2.8%
ValueCountFrequency (%)
145
 
4.5%
2285
28.5%
3424
42.4%
459
 
5.9%
597
 
9.7%
632
 
3.2%
728
 
2.8%
830
 
3.0%
ValueCountFrequency (%)
830
 
3.0%
728
 
2.8%
632
 
3.2%
597
 
9.7%
459
 
5.9%
3424
42.4%
2285
28.5%
145
 
4.5%
Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.253
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-12-12T20:44:50.990557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.805062118
Coefficient of variation (CV)0.4244209071
Kurtosis-0.5644492591
Mean4.253
Median Absolute Deviation (MAD)1
Skewness0.6197897812
Sum4253
Variance3.258249249
MonotonicityNot monotonic
2022-12-12T20:44:51.296916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3317
31.7%
5179
17.9%
2134
13.4%
4130
13.0%
686
 
8.6%
875
 
7.5%
768
 
6.8%
19
 
0.9%
92
 
0.2%
ValueCountFrequency (%)
19
 
0.9%
2134
13.4%
3317
31.7%
4130
13.0%
5179
17.9%
686
 
8.6%
768
 
6.8%
875
 
7.5%
92
 
0.2%
ValueCountFrequency (%)
92
 
0.2%
875
 
7.5%
768
 
6.8%
686
 
8.6%
5179
17.9%
4130
13.0%
3317
31.7%
2134
13.4%
19
 
0.9%
Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.536
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-12-12T20:44:51.668312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.898501596
Coefficient of variation (CV)0.4185409161
Kurtosis-0.8786599253
Mean4.536
Median Absolute Deviation (MAD)1
Skewness0.4763923327
Sum4536
Variance3.604308308
MonotonicityNot monotonic
2022-12-12T20:44:52.008678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3296
29.6%
5187
18.7%
4121
12.1%
2107
 
10.7%
8106
 
10.6%
787
 
8.7%
686
 
8.6%
16
 
0.6%
94
 
0.4%
ValueCountFrequency (%)
16
 
0.6%
2107
 
10.7%
3296
29.6%
4121
12.1%
5187
18.7%
686
 
8.6%
787
 
8.7%
8106
 
10.6%
94
 
0.4%
ValueCountFrequency (%)
94
 
0.4%
8106
 
10.6%
787
 
8.7%
686
 
8.6%
5187
18.7%
4121
12.1%
3296
29.6%
2107
 
10.7%
16
 
0.6%
Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.059
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-12-12T20:44:52.382524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q37
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.034615113
Coefficient of variation (CV)0.4021773301
Kurtosis-1.259246406
Mean5.059
Median Absolute Deviation (MAD)2
Skewness0.06273491791
Sum5059
Variance4.139658659
MonotonicityNot monotonic
2022-12-12T20:44:52.645570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3225
22.5%
5183
18.3%
8167
16.7%
7129
12.9%
6110
11.0%
293
9.3%
481
 
8.1%
16
 
0.6%
96
 
0.6%
ValueCountFrequency (%)
16
 
0.6%
293
9.3%
3225
22.5%
481
 
8.1%
5183
18.3%
6110
11.0%
7129
12.9%
8167
16.7%
96
 
0.6%
ValueCountFrequency (%)
96
 
0.6%
8167
16.7%
7129
12.9%
6110
11.0%
5183
18.3%
481
 
8.1%
3225
22.5%
293
9.3%
16
 
0.6%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Probably would purchase (4)
299 
Definitely would purchase (5)
293 
Might or might not purchase (3)
256 
Probably would not purchase (2)
114 
Definitely would not purchase (1)
38 

Length

Max length33
Median length31
Mean length29.294
Min length27

Characters and Unicode

Total characters29294
Distinct characters31
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDefinitely would purchase (5)
2nd rowProbably would purchase (4)
3rd rowProbably would purchase (4)
4th rowDefinitely would purchase (5)
5th rowDefinitely would purchase (5)

Common Values

ValueCountFrequency (%)
Probably would purchase (4)299
29.9%
Definitely would purchase (5)293
29.3%
Might or might not purchase (3)256
25.6%
Probably would not purchase (2)114
 
11.4%
Definitely would not purchase (1)38
 
3.8%

Length

2022-12-12T20:44:53.012022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:44:53.340193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
purchase1000
21.4%
would744
16.0%
might512
11.0%
probably413
8.9%
not408
8.7%
definitely331
 
7.1%
4299
 
6.4%
5293
 
6.3%
or256
 
5.5%
3256
 
5.5%
Other values (2)152
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3664
 
12.5%
o1821
 
6.2%
u1744
 
6.0%
r1669
 
5.7%
e1662
 
5.7%
h1512
 
5.2%
l1488
 
5.1%
a1413
 
4.8%
t1251
 
4.3%
i1174
 
4.0%
Other values (21)11896
40.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21630
73.8%
Space Separator3664
 
12.5%
Open Punctuation1000
 
3.4%
Close Punctuation1000
 
3.4%
Uppercase Letter1000
 
3.4%
Decimal Number1000
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1821
 
8.4%
u1744
 
8.1%
r1669
 
7.7%
e1662
 
7.7%
h1512
 
7.0%
l1488
 
6.9%
a1413
 
6.5%
t1251
 
5.8%
i1174
 
5.4%
p1000
 
4.6%
Other values (10)6896
31.9%
Decimal Number
ValueCountFrequency (%)
4299
29.9%
5293
29.3%
3256
25.6%
2114
 
11.4%
138
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
P413
41.3%
D331
33.1%
M256
25.6%
Space Separator
ValueCountFrequency (%)
3664
100.0%
Open Punctuation
ValueCountFrequency (%)
(1000
100.0%
Close Punctuation
ValueCountFrequency (%)
)1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22630
77.3%
Common6664
 
22.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o1821
 
8.0%
u1744
 
7.7%
r1669
 
7.4%
e1662
 
7.3%
h1512
 
6.7%
l1488
 
6.6%
a1413
 
6.2%
t1251
 
5.5%
i1174
 
5.2%
p1000
 
4.4%
Other values (13)7896
34.9%
Common
ValueCountFrequency (%)
3664
55.0%
(1000
 
15.0%
)1000
 
15.0%
4299
 
4.5%
5293
 
4.4%
3256
 
3.8%
2114
 
1.7%
138
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII29294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3664
 
12.5%
o1821
 
6.2%
u1744
 
6.0%
r1669
 
5.7%
e1662
 
5.7%
h1512
 
5.2%
l1488
 
5.1%
a1413
 
4.8%
t1251
 
4.3%
i1174
 
4.0%
Other values (21)11896
40.6%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
4
299 
5
293 
3
256 
2
114 
1
38 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
4299
29.9%
5293
29.3%
3256
25.6%
2114
 
11.4%
138
 
3.8%

Length

2022-12-12T20:44:53.737578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:44:54.013914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4299
29.9%
5293
29.3%
3256
25.6%
2114
 
11.4%
138
 
3.8%

Most occurring characters

ValueCountFrequency (%)
4299
29.9%
5293
29.3%
3256
25.6%
2114
 
11.4%
138
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4299
29.9%
5293
29.3%
3256
25.6%
2114
 
11.4%
138
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4299
29.9%
5293
29.3%
3256
25.6%
2114
 
11.4%
138
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4299
29.9%
5293
29.3%
3256
25.6%
2114
 
11.4%
138
 
3.8%
Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Like Very Much (8)
362 
Like Extremely (9)
258 
Like Moderately (7)
219 
Like Slightly (6)
110 
Neither Like nor Dislike (5)
 
35
Other values (4)
 
16

Length

Max length28
Median length18
Mean length18.496
Min length17

Characters and Unicode

Total characters18496
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowLike Extremely (9)
2nd rowLike Very Much (8)
3rd rowLike Moderately (7)
4th rowLike Extremely (9)
5th rowLike Extremely (9)

Common Values

ValueCountFrequency (%)
Like Very Much (8)362
36.2%
Like Extremely (9)258
25.8%
Like Moderately (7)219
21.9%
Like Slightly (6)110
 
11.0%
Neither Like nor Dislike (5)35
 
3.5%
Dislike Slightly (4)12
 
1.2%
Dislike Very Much (2)2
 
0.2%
Dislike Extremely (1)1
 
0.1%
Dislike Moderately (3)1
 
0.1%

Length

2022-12-12T20:44:54.273512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:44:54.742006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
like984
28.7%
very364
 
10.6%
much364
 
10.6%
8362
 
10.5%
extremely259
 
7.5%
9258
 
7.5%
moderately220
 
6.4%
7219
 
6.4%
slightly122
 
3.6%
6110
 
3.2%
Other values (8)172
 
5.0%

Most occurring characters

ValueCountFrequency (%)
2434
13.2%
e2427
13.1%
i1243
 
6.7%
k1035
 
5.6%
(1000
 
5.4%
)1000
 
5.4%
L984
 
5.3%
y965
 
5.2%
r913
 
4.9%
l774
 
4.2%
Other values (27)5721
30.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10663
57.7%
Space Separator2434
 
13.2%
Uppercase Letter2399
 
13.0%
Open Punctuation1000
 
5.4%
Close Punctuation1000
 
5.4%
Decimal Number1000
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2427
22.8%
i1243
11.7%
k1035
9.7%
y965
 
9.0%
r913
 
8.6%
l774
 
7.3%
t636
 
6.0%
h521
 
4.9%
u364
 
3.4%
c364
 
3.4%
Other values (8)1421
13.3%
Decimal Number
ValueCountFrequency (%)
8362
36.2%
9258
25.8%
7219
21.9%
6110
 
11.0%
535
 
3.5%
412
 
1.2%
22
 
0.2%
11
 
0.1%
31
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
L984
41.0%
M584
24.3%
V364
 
15.2%
E259
 
10.8%
S122
 
5.1%
D51
 
2.1%
N35
 
1.5%
Space Separator
ValueCountFrequency (%)
2434
100.0%
Open Punctuation
ValueCountFrequency (%)
(1000
100.0%
Close Punctuation
ValueCountFrequency (%)
)1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13062
70.6%
Common5434
29.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2427
18.6%
i1243
9.5%
k1035
 
7.9%
L984
 
7.5%
y965
 
7.4%
r913
 
7.0%
l774
 
5.9%
t636
 
4.9%
M584
 
4.5%
h521
 
4.0%
Other values (15)2980
22.8%
Common
ValueCountFrequency (%)
2434
44.8%
(1000
18.4%
)1000
18.4%
8362
 
6.7%
9258
 
4.7%
7219
 
4.0%
6110
 
2.0%
535
 
0.6%
412
 
0.2%
22
 
< 0.1%
Other values (2)2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII18496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2434
13.2%
e2427
13.1%
i1243
 
6.7%
k1035
 
5.6%
(1000
 
5.4%
)1000
 
5.4%
L984
 
5.3%
y965
 
5.2%
r913
 
4.9%
l774
 
4.2%
Other values (27)5721
30.9%
Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.642
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-12-12T20:44:55.178275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median8
Q39
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.201365589
Coefficient of variation (CV)0.1572056516
Kurtosis1.812746623
Mean7.642
Median Absolute Deviation (MAD)1
Skewness-1.075708348
Sum7642
Variance1.443279279
MonotonicityNot monotonic
2022-12-12T20:44:55.536476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8362
36.2%
9258
25.8%
7219
21.9%
6110
 
11.0%
535
 
3.5%
412
 
1.2%
22
 
0.2%
11
 
0.1%
31
 
0.1%
ValueCountFrequency (%)
11
 
0.1%
22
 
0.2%
31
 
0.1%
412
 
1.2%
535
 
3.5%
6110
 
11.0%
7219
21.9%
8362
36.2%
9258
25.8%
ValueCountFrequency (%)
9258
25.8%
8362
36.2%
7219
21.9%
6110
 
11.0%
535
 
3.5%
412
 
1.2%
31
 
0.1%
22
 
0.2%
11
 
0.1%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Just about right (3)
694 
Somewhat too little (2)
138 
Somewhat too much (4)
125 
Much too little (1)
 
22
Much too much (5)
 
21

Length

Max length23
Median length20
Mean length20.454
Min length17

Characters and Unicode

Total characters20454
Distinct characters26
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSomewhat too much (4)
2nd rowJust about right (3)
3rd rowJust about right (3)
4th rowJust about right (3)
5th rowJust about right (3)

Common Values

ValueCountFrequency (%)
Just about right (3)694
69.4%
Somewhat too little (2)138
 
13.8%
Somewhat too much (4)125
 
12.5%
Much too little (1)22
 
2.2%
Much too much (5)21
 
2.1%

Length

2022-12-12T20:44:55.925547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:44:56.344869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
just694
17.3%
about694
17.3%
right694
17.3%
3694
17.3%
too306
7.6%
somewhat263
 
6.6%
much189
 
4.7%
little160
 
4.0%
2138
 
3.5%
4125
 
3.1%
Other values (2)43
 
1.1%

Most occurring characters

ValueCountFrequency (%)
3000
14.7%
t2971
14.5%
u1577
 
7.7%
o1569
 
7.7%
h1146
 
5.6%
)1000
 
4.9%
(1000
 
4.9%
a957
 
4.7%
i854
 
4.2%
J694
 
3.4%
Other values (16)5686
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13454
65.8%
Space Separator3000
 
14.7%
Close Punctuation1000
 
4.9%
Open Punctuation1000
 
4.9%
Uppercase Letter1000
 
4.9%
Decimal Number1000
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t2971
22.1%
u1577
11.7%
o1569
11.7%
h1146
 
8.5%
a957
 
7.1%
i854
 
6.3%
g694
 
5.2%
r694
 
5.2%
b694
 
5.2%
s694
 
5.2%
Other values (5)1604
11.9%
Decimal Number
ValueCountFrequency (%)
3694
69.4%
2138
 
13.8%
4125
 
12.5%
122
 
2.2%
521
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
J694
69.4%
S263
 
26.3%
M43
 
4.3%
Space Separator
ValueCountFrequency (%)
3000
100.0%
Close Punctuation
ValueCountFrequency (%)
)1000
100.0%
Open Punctuation
ValueCountFrequency (%)
(1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14454
70.7%
Common6000
29.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t2971
20.6%
u1577
10.9%
o1569
10.9%
h1146
 
7.9%
a957
 
6.6%
i854
 
5.9%
J694
 
4.8%
g694
 
4.8%
r694
 
4.8%
b694
 
4.8%
Other values (8)2604
18.0%
Common
ValueCountFrequency (%)
3000
50.0%
)1000
 
16.7%
(1000
 
16.7%
3694
 
11.6%
2138
 
2.3%
4125
 
2.1%
122
 
0.4%
521
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII20454
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3000
14.7%
t2971
14.5%
u1577
 
7.7%
o1569
 
7.7%
h1146
 
5.6%
)1000
 
4.9%
(1000
 
4.9%
a957
 
4.7%
i854
 
4.2%
J694
 
3.4%
Other values (16)5686
27.8%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
3
694 
2
138 
4
125 
1
 
22
5
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3694
69.4%
2138
 
13.8%
4125
 
12.5%
122
 
2.2%
521
 
2.1%

Length

2022-12-12T20:44:56.686021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:44:56.958015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3694
69.4%
2138
 
13.8%
4125
 
12.5%
122
 
2.2%
521
 
2.1%

Most occurring characters

ValueCountFrequency (%)
3694
69.4%
2138
 
13.8%
4125
 
12.5%
122
 
2.2%
521
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3694
69.4%
2138
 
13.8%
4125
 
12.5%
122
 
2.2%
521
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3694
69.4%
2138
 
13.8%
4125
 
12.5%
122
 
2.2%
521
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3694
69.4%
2138
 
13.8%
4125
 
12.5%
122
 
2.2%
521
 
2.1%
Distinct9
Distinct (%)1.0%
Missing98
Missing (%)9.8%
Memory size7.9 KiB
Like Very Much (8)
277 
Like Moderately (7)
222 
Like Extremely (9)
197 
Like Slightly (6)
100 
Dislike Slightly (4)
36 
Other values (4)
70 

Length

Max length28
Median length18
Mean length18.64745011
Min length17

Characters and Unicode

Total characters16820
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLike Slightly (6)
2nd rowLike Very Much (8)
3rd rowLike Very Much (8)
4th rowLike Extremely (9)
5th rowLike Extremely (9)

Common Values

ValueCountFrequency (%)
Like Very Much (8)277
27.7%
Like Moderately (7)222
22.2%
Like Extremely (9)197
19.7%
Like Slightly (6)100
 
10.0%
Dislike Slightly (4)36
 
3.6%
Neither Like nor Dislike (5)23
 
2.3%
Dislike Very Much (2)19
 
1.9%
Dislike Moderately (3)19
 
1.9%
Dislike Extremely (1)9
 
0.9%
(Missing)98
 
9.8%

Length

2022-12-12T20:44:57.257506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:44:57.639760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
like819
26.9%
much296
 
9.7%
very296
 
9.7%
8277
 
9.1%
moderately241
 
7.9%
7222
 
7.3%
extremely206
 
6.8%
9197
 
6.5%
slightly136
 
4.5%
dislike106
 
3.5%
Other values (8)252
 
8.3%

Most occurring characters

ValueCountFrequency (%)
e2161
12.8%
2146
12.8%
i1190
 
7.1%
k925
 
5.5%
(902
 
5.4%
)902
 
5.4%
y879
 
5.2%
l825
 
4.9%
L819
 
4.9%
r789
 
4.7%
Other values (27)5282
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9845
58.5%
Space Separator2146
 
12.8%
Uppercase Letter2123
 
12.6%
Open Punctuation902
 
5.4%
Close Punctuation902
 
5.4%
Decimal Number902
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2161
22.0%
i1190
12.1%
k925
9.4%
y879
8.9%
l825
 
8.4%
r789
 
8.0%
t606
 
6.2%
h455
 
4.6%
c296
 
3.0%
u296
 
3.0%
Other values (8)1423
14.5%
Decimal Number
ValueCountFrequency (%)
8277
30.7%
7222
24.6%
9197
21.8%
6100
 
11.1%
436
 
4.0%
523
 
2.5%
219
 
2.1%
319
 
2.1%
19
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
L819
38.6%
M537
25.3%
V296
 
13.9%
E206
 
9.7%
S136
 
6.4%
D106
 
5.0%
N23
 
1.1%
Space Separator
ValueCountFrequency (%)
2146
100.0%
Open Punctuation
ValueCountFrequency (%)
(902
100.0%
Close Punctuation
ValueCountFrequency (%)
)902
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11968
71.2%
Common4852
28.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2161
18.1%
i1190
9.9%
k925
 
7.7%
y879
 
7.3%
l825
 
6.9%
L819
 
6.8%
r789
 
6.6%
t606
 
5.1%
M537
 
4.5%
h455
 
3.8%
Other values (15)2782
23.2%
Common
ValueCountFrequency (%)
2146
44.2%
(902
18.6%
)902
18.6%
8277
 
5.7%
7222
 
4.6%
9197
 
4.1%
6100
 
2.1%
436
 
0.7%
523
 
0.5%
219
 
0.4%
Other values (2)28
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII16820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2161
12.8%
2146
12.8%
i1190
 
7.1%
k925
 
5.5%
(902
 
5.4%
)902
 
5.4%
y879
 
5.2%
l825
 
4.9%
L819
 
4.9%
r789
 
4.7%
Other values (27)5282
31.4%

Taste the products. How do you like the products OVERALL? - value
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct9
Distinct (%)1.0%
Missing98
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean7.21286031
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-12-12T20:44:58.041477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median8
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.712761665
Coefficient of variation (CV)0.2374594254
Kurtosis2.202003011
Mean7.21286031
Median Absolute Deviation (MAD)1
Skewness-1.468791199
Sum6506
Variance2.93355252
MonotonicityNot monotonic
2022-12-12T20:44:58.394578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8277
27.7%
7222
22.2%
9197
19.7%
6100
 
10.0%
436
 
3.6%
523
 
2.3%
219
 
1.9%
319
 
1.9%
19
 
0.9%
(Missing)98
 
9.8%
ValueCountFrequency (%)
19
 
0.9%
219
 
1.9%
319
 
1.9%
436
 
3.6%
523
 
2.3%
6100
 
10.0%
7222
22.2%
8277
27.7%
9197
19.7%
ValueCountFrequency (%)
9197
19.7%
8277
27.7%
7222
22.2%
6100
 
10.0%
523
 
2.3%
436
 
3.6%
319
 
1.9%
219
 
1.9%
19
 
0.9%
Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Like Very Much (8)
316 
Like Extremely (9)
235 
Like Moderately (7)
222 
Like Slightly (6)
108 
Dislike Slightly (4)
44 
Other values (4)
75 

Length

Max length28
Median length18
Mean length18.628
Min length17

Characters and Unicode

Total characters18628
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLike Extremely (9)
2nd rowLike Very Much (8)
3rd rowLike Moderately (7)
4th rowLike Extremely (9)
5th rowLike Extremely (9)

Common Values

ValueCountFrequency (%)
Like Very Much (8)316
31.6%
Like Extremely (9)235
23.5%
Like Moderately (7)222
22.2%
Like Slightly (6)108
 
10.8%
Dislike Slightly (4)44
 
4.4%
Neither Like nor Dislike (5)26
 
2.6%
Dislike Very Much (2)20
 
2.0%
Dislike Moderately (3)19
 
1.9%
Dislike Extremely (1)10
 
1.0%

Length

2022-12-12T20:44:58.769690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:44:59.091451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
like907
26.8%
much336
 
9.9%
very336
 
9.9%
8316
 
9.3%
extremely245
 
7.2%
moderately241
 
7.1%
9235
 
6.9%
7222
 
6.6%
slightly152
 
4.5%
dislike119
 
3.5%
Other values (8)279
 
8.2%

Most occurring characters

ValueCountFrequency (%)
2388
12.8%
e2386
12.8%
i1323
 
7.1%
k1026
 
5.5%
(1000
 
5.4%
)1000
 
5.4%
y974
 
5.2%
l909
 
4.9%
L907
 
4.9%
r874
 
4.7%
Other values (27)5841
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10878
58.4%
Space Separator2388
 
12.8%
Uppercase Letter2362
 
12.7%
Open Punctuation1000
 
5.4%
Close Punctuation1000
 
5.4%
Decimal Number1000
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2386
21.9%
i1323
12.2%
k1026
9.4%
y974
9.0%
l909
 
8.4%
r874
 
8.0%
t664
 
6.1%
h514
 
4.7%
c336
 
3.1%
u336
 
3.1%
Other values (8)1536
14.1%
Decimal Number
ValueCountFrequency (%)
8316
31.6%
9235
23.5%
7222
22.2%
6108
 
10.8%
444
 
4.4%
526
 
2.6%
220
 
2.0%
319
 
1.9%
110
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
L907
38.4%
M577
24.4%
V336
 
14.2%
E245
 
10.4%
S152
 
6.4%
D119
 
5.0%
N26
 
1.1%
Space Separator
ValueCountFrequency (%)
2388
100.0%
Open Punctuation
ValueCountFrequency (%)
(1000
100.0%
Close Punctuation
ValueCountFrequency (%)
)1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13240
71.1%
Common5388
28.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2386
18.0%
i1323
10.0%
k1026
 
7.7%
y974
 
7.4%
l909
 
6.9%
L907
 
6.9%
r874
 
6.6%
t664
 
5.0%
M577
 
4.4%
h514
 
3.9%
Other values (15)3086
23.3%
Common
ValueCountFrequency (%)
2388
44.3%
(1000
18.6%
)1000
18.6%
8316
 
5.9%
9235
 
4.4%
7222
 
4.1%
6108
 
2.0%
444
 
0.8%
526
 
0.5%
220
 
0.4%
Other values (2)29
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII18628
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2388
12.8%
e2386
12.8%
i1323
 
7.1%
k1026
 
5.5%
(1000
 
5.4%
)1000
 
5.4%
y974
 
5.2%
l909
 
4.9%
L907
 
4.9%
r874
 
4.7%
Other values (27)5841
31.4%
Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.258
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-12-12T20:44:59.409865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median8
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.721743419
Coefficient of variation (CV)0.2372200908
Kurtosis2.151674685
Mean7.258
Median Absolute Deviation (MAD)1
Skewness-1.472560828
Sum7258
Variance2.9644004
MonotonicityNot monotonic
2022-12-12T20:44:59.748410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8316
31.6%
9235
23.5%
7222
22.2%
6108
 
10.8%
444
 
4.4%
526
 
2.6%
220
 
2.0%
319
 
1.9%
110
 
1.0%
ValueCountFrequency (%)
110
 
1.0%
220
 
2.0%
319
 
1.9%
444
 
4.4%
526
 
2.6%
6108
 
10.8%
7222
22.2%
8316
31.6%
9235
23.5%
ValueCountFrequency (%)
9235
23.5%
8316
31.6%
7222
22.2%
6108
 
10.8%
526
 
2.6%
444
 
4.4%
319
 
1.9%
220
 
2.0%
110
 
1.0%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Just about right (3)
660 
Not quite strong enough (2)
178 
Slightly too strong (4)
123 
Not at all strong enough (1)
 
23
Much too strong (5)
 
16

Length

Max length28
Median length20
Mean length21.783
Min length19

Characters and Unicode

Total characters21783
Distinct characters28
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSlightly too strong (4)
2nd rowJust about right (3)
3rd rowJust about right (3)
4th rowJust about right (3)
5th rowJust about right (3)

Common Values

ValueCountFrequency (%)
Just about right (3)660
66.0%
Not quite strong enough (2)178
 
17.8%
Slightly too strong (4)123
 
12.3%
Not at all strong enough (1)23
 
2.3%
Much too strong (5)16
 
1.6%

Length

2022-12-12T20:45:00.112097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:45:00.580100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
just660
15.6%
right660
15.6%
3660
15.6%
about660
15.6%
strong340
8.0%
enough201
 
4.8%
not201
 
4.8%
2178
 
4.2%
quite178
 
4.2%
too139
 
3.3%
Other values (7)347
8.2%

Most occurring characters

ValueCountFrequency (%)
3224
14.8%
t2984
13.7%
u1715
 
7.9%
o1680
 
7.7%
g1324
 
6.1%
)1000
 
4.6%
s1000
 
4.6%
r1000
 
4.6%
h1000
 
4.6%
(1000
 
4.6%
Other values (18)5856
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14559
66.8%
Space Separator3224
 
14.8%
Close Punctuation1000
 
4.6%
Open Punctuation1000
 
4.6%
Decimal Number1000
 
4.6%
Uppercase Letter1000
 
4.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t2984
20.5%
u1715
11.8%
o1680
11.5%
g1324
9.1%
s1000
 
6.9%
r1000
 
6.9%
h1000
 
6.9%
i961
 
6.6%
a706
 
4.8%
b660
 
4.5%
Other values (6)1529
10.5%
Decimal Number
ValueCountFrequency (%)
3660
66.0%
2178
 
17.8%
4123
 
12.3%
123
 
2.3%
516
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
J660
66.0%
N201
 
20.1%
S123
 
12.3%
M16
 
1.6%
Space Separator
ValueCountFrequency (%)
3224
100.0%
Close Punctuation
ValueCountFrequency (%)
)1000
100.0%
Open Punctuation
ValueCountFrequency (%)
(1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15559
71.4%
Common6224
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t2984
19.2%
u1715
11.0%
o1680
10.8%
g1324
8.5%
s1000
 
6.4%
r1000
 
6.4%
h1000
 
6.4%
i961
 
6.2%
a706
 
4.5%
J660
 
4.2%
Other values (10)2529
16.3%
Common
ValueCountFrequency (%)
3224
51.8%
)1000
 
16.1%
(1000
 
16.1%
3660
 
10.6%
2178
 
2.9%
4123
 
2.0%
123
 
0.4%
516
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII21783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3224
14.8%
t2984
13.7%
u1715
 
7.9%
o1680
 
7.7%
g1324
 
6.1%
)1000
 
4.6%
s1000
 
4.6%
r1000
 
4.6%
h1000
 
4.6%
(1000
 
4.6%
Other values (18)5856
26.9%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
3
660 
2
178 
4
123 
1
 
23
5
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3660
66.0%
2178
 
17.8%
4123
 
12.3%
123
 
2.3%
516
 
1.6%

Length

2022-12-12T20:45:00.995447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:45:01.386609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3660
66.0%
2178
 
17.8%
4123
 
12.3%
123
 
2.3%
516
 
1.6%

Most occurring characters

ValueCountFrequency (%)
3660
66.0%
2178
 
17.8%
4123
 
12.3%
123
 
2.3%
516
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3660
66.0%
2178
 
17.8%
4123
 
12.3%
123
 
2.3%
516
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3660
66.0%
2178
 
17.8%
4123
 
12.3%
123
 
2.3%
516
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3660
66.0%
2178
 
17.8%
4123
 
12.3%
123
 
2.3%
516
 
1.6%
Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Like Very Much (8)
332 
Like Extremely (9)
264 
Like Moderately (7)
213 
Like Slightly (6)
100 
Neither Like nor Dislike (5)
42 
Other values (4)
49 

Length

Max length28
Median length18
Mean length18.66
Min length17

Characters and Unicode

Total characters18660
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowLike Extremely (9)
2nd rowLike Very Much (8)
3rd rowLike Moderately (7)
4th rowLike Extremely (9)
5th rowLike Extremely (9)

Common Values

ValueCountFrequency (%)
Like Very Much (8)332
33.2%
Like Extremely (9)264
26.4%
Like Moderately (7)213
21.3%
Like Slightly (6)100
 
10.0%
Neither Like nor Dislike (5)42
 
4.2%
Dislike Slightly (4)32
 
3.2%
Dislike Moderately (3)12
 
1.2%
Dislike Very Much (2)4
 
0.4%
Dislike Extremely (1)1
 
0.1%

Length

2022-12-12T20:45:01.773565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:45:02.236330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
like951
27.8%
very336
 
9.8%
much336
 
9.8%
8332
 
9.7%
extremely265
 
7.7%
9264
 
7.7%
moderately225
 
6.6%
7213
 
6.2%
slightly132
 
3.9%
6100
 
2.9%
Other values (8)266
 
7.8%

Most occurring characters

ValueCountFrequency (%)
e2442
13.1%
2420
13.0%
i1307
 
7.0%
k1042
 
5.6%
(1000
 
5.4%
)1000
 
5.4%
y958
 
5.1%
L951
 
5.1%
r910
 
4.9%
l845
 
4.5%
Other values (27)5785
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10862
58.2%
Space Separator2420
 
13.0%
Uppercase Letter2378
 
12.7%
Open Punctuation1000
 
5.4%
Close Punctuation1000
 
5.4%
Decimal Number1000
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2442
22.5%
i1307
12.0%
k1042
9.6%
y958
 
8.8%
r910
 
8.4%
l845
 
7.8%
t664
 
6.1%
h510
 
4.7%
c336
 
3.1%
u336
 
3.1%
Other values (8)1512
13.9%
Decimal Number
ValueCountFrequency (%)
8332
33.2%
9264
26.4%
7213
21.3%
6100
 
10.0%
542
 
4.2%
432
 
3.2%
312
 
1.2%
24
 
0.4%
11
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
L951
40.0%
M561
23.6%
V336
 
14.1%
E265
 
11.1%
S132
 
5.6%
D91
 
3.8%
N42
 
1.8%
Space Separator
ValueCountFrequency (%)
2420
100.0%
Open Punctuation
ValueCountFrequency (%)
(1000
100.0%
Close Punctuation
ValueCountFrequency (%)
)1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13240
71.0%
Common5420
29.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2442
18.4%
i1307
9.9%
k1042
 
7.9%
y958
 
7.2%
L951
 
7.2%
r910
 
6.9%
l845
 
6.4%
t664
 
5.0%
M561
 
4.2%
h510
 
3.9%
Other values (15)3050
23.0%
Common
ValueCountFrequency (%)
2420
44.6%
(1000
18.5%
)1000
18.5%
8332
 
6.1%
9264
 
4.9%
7213
 
3.9%
6100
 
1.8%
542
 
0.8%
432
 
0.6%
312
 
0.2%
Other values (2)5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII18660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2442
13.1%
2420
13.0%
i1307
 
7.0%
k1042
 
5.6%
(1000
 
5.4%
)1000
 
5.4%
y958
 
5.1%
L951
 
5.1%
r910
 
4.9%
l845
 
4.5%
Other values (27)5785
31.0%
Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.506
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-12-12T20:45:02.661925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median8
Q39
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.420556927
Coefficient of variation (CV)0.1892561853
Kurtosis1.632567546
Mean7.506
Median Absolute Deviation (MAD)1
Skewness-1.235027458
Sum7506
Variance2.017981982
MonotonicityNot monotonic
2022-12-12T20:45:02.907733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8332
33.2%
9264
26.4%
7213
21.3%
6100
 
10.0%
542
 
4.2%
432
 
3.2%
312
 
1.2%
24
 
0.4%
11
 
0.1%
ValueCountFrequency (%)
11
 
0.1%
24
 
0.4%
312
 
1.2%
432
 
3.2%
542
 
4.2%
6100
 
10.0%
7213
21.3%
8332
33.2%
9264
26.4%
ValueCountFrequency (%)
9264
26.4%
8332
33.2%
7213
21.3%
6100
 
10.0%
542
 
4.2%
432
 
3.2%
312
 
1.2%
24
 
0.4%
11
 
0.1%
Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Like Very Much (8)
232 
Like Moderately (7)
209 
Like Extremely (9)
173 
Neither Like nor Dislike (5)
128 
Like Slightly (6)
114 
Other values (4)
144 

Length

Max length28
Median length22
Mean length19.748
Min length17

Characters and Unicode

Total characters19748
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLike Very Much (8)
2nd rowLike Very Much (8)
3rd rowLike Moderately (7)
4th rowLike Extremely (9)
5th rowLike Very Much (8)

Common Values

ValueCountFrequency (%)
Like Very Much (8)232
23.2%
Like Moderately (7)209
20.9%
Like Extremely (9)173
17.3%
Neither Like nor Dislike (5)128
12.8%
Like Slightly (6)114
11.4%
Dislike Slightly (4)86
 
8.6%
Dislike Moderately (3)27
 
2.7%
Dislike Very Much (2)17
 
1.7%
Dislike Extremely (1)14
 
1.4%

Length

2022-12-12T20:45:03.246381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:45:03.683042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
like856
24.4%
dislike272
 
7.8%
much249
 
7.1%
very249
 
7.1%
moderately236
 
6.7%
8232
 
6.6%
7209
 
6.0%
slightly200
 
5.7%
extremely187
 
5.3%
9173
 
4.9%
Other values (8)642
18.3%

Most occurring characters

ValueCountFrequency (%)
2505
12.7%
e2479
12.6%
i1728
 
8.8%
k1128
 
5.7%
l1095
 
5.5%
(1000
 
5.1%
)1000
 
5.1%
r928
 
4.7%
y872
 
4.4%
L856
 
4.3%
Other values (27)6157
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11866
60.1%
Space Separator2505
 
12.7%
Uppercase Letter2377
 
12.0%
Open Punctuation1000
 
5.1%
Close Punctuation1000
 
5.1%
Decimal Number1000
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2479
20.9%
i1728
14.6%
k1128
9.5%
l1095
9.2%
r928
 
7.8%
y872
 
7.3%
t751
 
6.3%
h577
 
4.9%
o364
 
3.1%
s272
 
2.3%
Other values (8)1672
14.1%
Decimal Number
ValueCountFrequency (%)
8232
23.2%
7209
20.9%
9173
17.3%
5128
12.8%
6114
11.4%
486
 
8.6%
327
 
2.7%
217
 
1.7%
114
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
L856
36.0%
M485
20.4%
D272
 
11.4%
V249
 
10.5%
S200
 
8.4%
E187
 
7.9%
N128
 
5.4%
Space Separator
ValueCountFrequency (%)
2505
100.0%
Open Punctuation
ValueCountFrequency (%)
(1000
100.0%
Close Punctuation
ValueCountFrequency (%)
)1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14243
72.1%
Common5505
 
27.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2479
17.4%
i1728
12.1%
k1128
 
7.9%
l1095
 
7.7%
r928
 
6.5%
y872
 
6.1%
L856
 
6.0%
t751
 
5.3%
h577
 
4.1%
M485
 
3.4%
Other values (15)3344
23.5%
Common
ValueCountFrequency (%)
2505
45.5%
(1000
 
18.2%
)1000
 
18.2%
8232
 
4.2%
7209
 
3.8%
9173
 
3.1%
5128
 
2.3%
6114
 
2.1%
486
 
1.6%
327
 
0.5%
Other values (2)31
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII19748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2505
12.7%
e2479
12.6%
i1728
 
8.8%
k1128
 
5.7%
l1095
 
5.5%
(1000
 
5.1%
)1000
 
5.1%
r928
 
4.7%
y872
 
4.4%
L856
 
4.3%
Other values (27)6157
31.2%
Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.673
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-12-12T20:45:04.080669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median7
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.892525469
Coefficient of variation (CV)0.2836093915
Kurtosis0.0314627327
Mean6.673
Median Absolute Deviation (MAD)1
Skewness-0.7658938037
Sum6673
Variance3.581652653
MonotonicityNot monotonic
2022-12-12T20:45:04.343993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8232
23.2%
7209
20.9%
9173
17.3%
5128
12.8%
6114
11.4%
486
 
8.6%
327
 
2.7%
217
 
1.7%
114
 
1.4%
ValueCountFrequency (%)
114
 
1.4%
217
 
1.7%
327
 
2.7%
486
 
8.6%
5128
12.8%
6114
11.4%
7209
20.9%
8232
23.2%
9173
17.3%
ValueCountFrequency (%)
9173
17.3%
8232
23.2%
7209
20.9%
6114
11.4%
5128
12.8%
486
 
8.6%
327
 
2.7%
217
 
1.7%
114
 
1.4%
Distinct5
Distinct (%)0.5%
Missing87
Missing (%)8.7%
Memory size7.9 KiB
Definitely would purchase (5)
353 
Probably would purchase (4)
254 
Might or might not purchase (3)
142 
Probably would not purchase (2)
99 
Definitely would not purchase (1)
65 

Length

Max length33
Median length31
Mean length29.25629792
Min length27

Characters and Unicode

Total characters26711
Distinct characters31
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProbably would purchase (4)
2nd rowProbably would purchase (4)
3rd rowProbably would purchase (4)
4th rowDefinitely would purchase (5)
5th rowDefinitely would purchase (5)

Common Values

ValueCountFrequency (%)
Definitely would purchase (5)353
35.3%
Probably would purchase (4)254
25.4%
Might or might not purchase (3)142
14.2%
Probably would not purchase (2)99
 
9.9%
Definitely would not purchase (1)65
 
6.5%
(Missing)87
 
8.7%

Length

2022-12-12T20:45:04.739326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:45:05.180813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
purchase913
22.3%
would771
18.8%
definitely418
10.2%
5353
 
8.6%
probably353
 
8.6%
not306
 
7.5%
might284
 
6.9%
4254
 
6.2%
or142
 
3.5%
3142
 
3.5%
Other values (2)164
 
4.0%

Most occurring characters

ValueCountFrequency (%)
3187
 
11.9%
e1749
 
6.5%
u1684
 
6.3%
o1572
 
5.9%
l1542
 
5.8%
r1408
 
5.3%
a1266
 
4.7%
h1197
 
4.5%
i1120
 
4.2%
t1008
 
3.8%
Other values (21)10978
41.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter19872
74.4%
Space Separator3187
 
11.9%
Close Punctuation913
 
3.4%
Open Punctuation913
 
3.4%
Uppercase Letter913
 
3.4%
Decimal Number913
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1749
 
8.8%
u1684
 
8.5%
o1572
 
7.9%
l1542
 
7.8%
r1408
 
7.1%
a1266
 
6.4%
h1197
 
6.0%
i1120
 
5.6%
t1008
 
5.1%
p913
 
4.6%
Other values (10)6413
32.3%
Decimal Number
ValueCountFrequency (%)
5353
38.7%
4254
27.8%
3142
15.6%
299
 
10.8%
165
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
D418
45.8%
P353
38.7%
M142
 
15.6%
Space Separator
ValueCountFrequency (%)
3187
100.0%
Close Punctuation
ValueCountFrequency (%)
)913
100.0%
Open Punctuation
ValueCountFrequency (%)
(913
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin20785
77.8%
Common5926
 
22.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1749
 
8.4%
u1684
 
8.1%
o1572
 
7.6%
l1542
 
7.4%
r1408
 
6.8%
a1266
 
6.1%
h1197
 
5.8%
i1120
 
5.4%
t1008
 
4.8%
p913
 
4.4%
Other values (13)7326
35.2%
Common
ValueCountFrequency (%)
3187
53.8%
)913
 
15.4%
(913
 
15.4%
5353
 
6.0%
4254
 
4.3%
3142
 
2.4%
299
 
1.7%
165
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII26711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3187
 
11.9%
e1749
 
6.5%
u1684
 
6.3%
o1572
 
5.9%
l1542
 
5.8%
r1408
 
5.3%
a1266
 
4.7%
h1197
 
4.5%
i1120
 
4.2%
t1008
 
3.8%
Other values (21)10978
41.1%
Distinct5
Distinct (%)0.5%
Missing87
Missing (%)8.7%
Memory size7.9 KiB
5.0
353 
4.0
254 
3.0
142 
2.0
99 
1.0
65 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2739
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0353
35.3%
4.0254
25.4%
3.0142
14.2%
2.099
 
9.9%
1.065
 
6.5%
(Missing)87
 
8.7%

Length

2022-12-12T20:45:06.010012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T20:45:06.350760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
5.0353
38.7%
4.0254
27.8%
3.0142
15.6%
2.099
 
10.8%
1.065
 
7.1%

Most occurring characters

ValueCountFrequency (%)
.913
33.3%
0913
33.3%
5353
 
12.9%
4254
 
9.3%
3142
 
5.2%
299
 
3.6%
165
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1826
66.7%
Other Punctuation913
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0913
50.0%
5353
 
19.3%
4254
 
13.9%
3142
 
7.8%
299
 
5.4%
165
 
3.6%
Other Punctuation
ValueCountFrequency (%)
.913
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2739
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.913
33.3%
0913
33.3%
5353
 
12.9%
4254
 
9.3%
3142
 
5.2%
299
 
3.6%
165
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.913
33.3%
0913
33.3%
5353
 
12.9%
4254
 
9.3%
3142
 
5.2%
299
 
3.6%
165
 
2.4%

Interactions

2022-12-12T20:44:26.699049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:52.029865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:58.570039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:05.535146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:12.479486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:19.181952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:25.869666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:33.069361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:39.730290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:46.337221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:52.916971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:59.992990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:06.622041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:13.112028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:20.210603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:27.078841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:52.619291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:59.034276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:05.932527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:12.899174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:19.662847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:26.281389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:33.511882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:40.140664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:46.756529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:53.333290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:00.408252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:07.009789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:13.506395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:20.601777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:27.524306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:53.080642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:59.513595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:06.407005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:13.337367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:20.110680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:26.741770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:33.962678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:40.554282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:47.190143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:53.740259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:00.868150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:07.455508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:13.880880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:21.012704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:27.972373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:53.495905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:59.968051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:06.839261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:13.775721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:20.539798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:27.179931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:34.402517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:40.946510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:47.625428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:54.192786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:01.329035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:07.878134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:14.298564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:21.448938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:28.389384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:53.906929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:00.429608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:07.297217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:14.235823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:20.985304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:27.664576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:34.882984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:41.395956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:48.074757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:54.636233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:01.743769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:08.255570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:14.721255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:21.886492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:28.836771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:54.348629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:00.831523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:07.715633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:14.714004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:21.421046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:28.105686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:35.332436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:41.984316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:48.536421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:55.051945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:02.192672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:08.722202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:15.150785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:22.322277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:29.287616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:54.793378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:01.260806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:08.554589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:15.154517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:21.852195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:28.523519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:35.772273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:42.429926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:48.952913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:55.465604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:02.649089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:09.179307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:15.747526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:22.764888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:29.848868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:55.218439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:01.720942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:08.987231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:15.642834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:22.306145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:28.961834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:36.205438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:42.874694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:49.341382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:56.415285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:03.092127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:09.660760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:16.162969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:23.193425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:30.405166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:55.662462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:02.157066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:09.455318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:16.077225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:22.749264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:29.431312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:36.652906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:43.328176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:49.783326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:56.842393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:03.548665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:10.075924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:16.626554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:23.581751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:30.895957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:56.071239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:02.608104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:09.899556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:16.507728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:23.191573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:29.880075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:37.075249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:43.783895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:50.227134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:57.254073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:03.929042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:10.514539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:17.073201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:24.035793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:31.450614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:56.507375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:03.128794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:10.297393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:16.925937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:23.629136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:30.340431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:37.509861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:44.192227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:50.673317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:57.736042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:04.316936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:10.971092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:17.521053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:24.467716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:31.978886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:56.912482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:03.566007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:10.768902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:17.386654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:24.066852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:30.809450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:37.949029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:44.591779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:51.124677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:58.160813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:04.752880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:11.409347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:17.971900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:24.896897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:32.386607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:57.316332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:04.060570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:11.177925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:17.805700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:24.538499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:31.259708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:38.416177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:45.032664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:51.598697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:58.626500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:05.205601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:11.817037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:18.421114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:25.329618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:32.828974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:57.745138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:04.513192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:11.610888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:18.245940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:25.012318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:31.722290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:38.878269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:45.486740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:52.050913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:59.045879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:05.677446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:12.247444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:18.883920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:25.777508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:33.241951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:42:58.139370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:04.947860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:12.023913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:18.712755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:25.446764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:32.647537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:39.287368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:45.911915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:52.484907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:43:59.501559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:06.126933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:12.676034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:19.776961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T20:44:26.227915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-12-12T20:45:06.796150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-12-12T20:45:08.217713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-12T20:45:09.221879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-12T20:45:10.270607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-12T20:45:11.265176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-12T20:45:12.187603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-12T20:44:34.070247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-12T20:44:37.845698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-12T20:44:39.084787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-12-12T20:44:39.663815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.